了解caffe,使用caffe,Be familiar with a small examplecaffe.
caffe Github： https://github.com/BVLC/caffe
caffe Tutorial :https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.gc2fcdcce7_216_498
why caffe： only for vision; very simple.caffeModels can be written without knowing programming.
擁有一個docker環境：https://github.com/BVLC/caffe/tree/master/docker
caffe mnist Tutorial :https://caffe.berkeleyvision.org/gathered/examples/mnist.html
微信二維碼opencv模型文件：https://github.com/WeChatCV/opencv_3rdparty
微信二維碼opencv源碼：https://github.com/opencv/opencv_contrib/blob/master/modules/wechat_qrcode/src/wechat_qrcode.cpp#L156
Introduction to WeChat QR code technology：https://zhuanlan.zhihu.com/p/348412200
cpu:
cd ~
mkdir caffetest
docker run -ti -v ~/caffetest/:/app bvlc/caffe:cpu bash
代碼下載下來,宿主機執行：
cd $CAFFE_ROOT
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
I did not compile and installcaffe,執行create_mnist.sh報錯,lmdbHow sacred is the data format？：
caffeDirectly processed data is divided into two formats： lmdb和leveldb,兩者關系： They are all keys/Value pair embedded database management system programming library
雖然lmdbThe memory consumption is leveldb的1.1倍,但是lmdb的處理速度比leveldb快10%到15%,另外lmdbAllows multiple training models to read the same dataset at the same time.
lmdb取代了leveldb成為了caffeDefault dataset generation format.
docker 內的LinuxRelease version viewcat /etc/issue,
Ubuntu 16.04.4 LTS \n \l
dockerfile:https://github.com/BVLC/caffe/blob/9b891540183ddc834a02b2bd81b31afae71b2153/docker/cpu/Dockerfile
ubuntu安裝教程：http://caffe.berkeleyvision.org/install_apt.html
ubuntu的update太慢了,Take this road later.
我想在pythonIt calls the code for QR code detection,No need to recognize the QR code,Only object detection is performed.
在線可視化prototxt模型：http://ethereon.github.io/netscope/#/editor
看源碼：
The processing of the input image is required：
（1）Too young to not,This is for QR code decoding,Regardless of this step.
（2）灰度化圖片.
（3）歸一化圖片.
（4）得輸出.
import math
import cv2
import numpy as np
cvNet = cv2.dnn.readNetFromCaffe('detect.prototxt', 'detect.caffemodel')
image_path = r'C:\Users\dong.xie\Desktop\workcode\kevintest\google_crawler_for_linux\src\生活 二維碼_1\img000001.jpg'
img2 = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), 1) # img是矩陣
img = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
img_w = img.shape[1]
img_h = img.shape[0]
minInputSize = 400
resizeRatio = math.sqrt(img_w * img_h * 1.0 / (minInputSize * minInputSize))
target_width = int(img_w / resizeRatio)
target_height = int(img_h / resizeRatio)
input = cv2.resize(img, (target_width, target_height), interpolation=cv2.INTER_CUBIC)
blob = cv2.dnn.blobFromImage(input, scalefactor=1.0 / 255, size=(input.shape[1], input.shape[0]), mean=(0, 0, 0),
swapRB=False, crop=False)
cvNet.setInput(blob, name="data")
prob = cvNet.forward()
print(prob.shape)
# define CLIP(x, x1, x2) max(x1, min(x, x2))
def CLIP(x, x1, x2):
return int(max(x1, min(x, x2)))
# theshape is (1, 1, 100, 7) = > (batch, channel, count, dim)
for row in range(0, prob.shape[2]):
prob_score = prob[0][0][row]
# prob_score[0] is not used.
# prob_score[1]==1 stands for qrcode
# add a safe score threshold due to https://github.com/opencv/opencv_contrib/issues/2877
# prob_score[2] is the probability of the qrcode, which is not used.
if prob_score[1] == 1 and prob_score[2] > 1E-5:
x0 = CLIP(prob_score[3] * img_w, 0.0, img_w - 1.0)
y0 = CLIP(prob_score[4] * img_h, 0.0, img_h - 1.0)
x1 = CLIP(prob_score[5] * img_w, 0.0, img_w - 1.0)
y1 = CLIP(prob_score[6] * img_h, 0.0, img_h - 1.0)
cv2.rectangle(img2, (x0, y0), (x1, y1), (0, 0, 255), 2)
cv2.imshow("1", img2)
cv2.waitKey()
cv2.destroyAllWindows()
還是挺成功的：
# pip install opencv-contrib-python
import math
import os
import traceback
import cv2
import numpy as np
dataFile = r'''C:\Users\dong.xie\Desktop\12312'''
dst = r'E:\detection\02二維碼\images'
dst1 = r'E:\detection\02二維碼\labels'
cnt = 3000
def listPathAllfiles(dirname):
result = []
for maindir, subdir, file_name_list in os.walk(dirname):
for filename in file_name_list:
apath = os.path.join(maindir, filename)
result.append(apath)
return result
if __name__ == '__main__':
files = listPathAllfiles(dataFile)
cvNet = cv2.dnn.readNetFromCaffe('detect.prototxt', 'detect.caffemodel')
for file in files:
try:
if file.endswith(".jpg") or file.endswith(".png"):
img2 = cv2.imdecode(np.fromfile(file, dtype=np.uint8), 1) # img是矩陣
img = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
img_w = img.shape[1]
img_h = img.shape[0]
minInputSize = 400
resizeRatio = math.sqrt(img_w * img_h * 1.0 / (minInputSize * minInputSize))
target_width = int(img_w / resizeRatio)
target_height = int(img_h / resizeRatio)
input = cv2.resize(img, (target_width, target_height), interpolation=cv2.INTER_CUBIC)
blob = cv2.dnn.blobFromImage(input, scalefactor=1.0 / 255, size=(input.shape[1], input.shape[0]),
mean=(0, 0, 0),
swapRB=False, crop=False)
cvNet.setInput(blob, name="data")
prob = cvNet.forward()
# print(prob.shape)
# define CLIP(x, x1, x2) max(x1, min(x, x2))
def CLIP(x, x1, x2):
return int(max(x1, min(x, x2)))
resDet = []
# theshape is (1, 1, 100, 7) = > (batch, channel, count, dim)
for row in range(0, prob.shape[2]):
prob_score = prob[0][0][row]
# prob_score[0] is not used.
# prob_score[1]==1 stands for qrcode
# add a safe score threshold due to https://github.com/opencv/opencv_contrib/issues/2877
# prob_score[2] is the probability of the qrcode, which is not used.
if prob_score[1] == 1 and prob_score[2] > 1E-5:
x0 = CLIP(prob_score[3] * img_w, 0.0, img_w - 1.0)
y0 = CLIP(prob_score[4] * img_h, 0.0, img_h - 1.0)
x1 = CLIP(prob_score[5] * img_w, 0.0, img_w - 1.0)
y1 = CLIP(prob_score[6] * img_h, 0.0, img_h - 1.0)
resDet.append([x0, y0, x1, y1])
if len(resDet) >= 1:
cv2.imencode('.jpg', img2)[1].tofile(os.path.join(dst, "qrcode_" + str(cnt).zfill(6) + ".jpg"))
txtfile = os.path.join(dst1, "qrcode_" + str(cnt).zfill(6) + ".txt")
res = []
for x0, y0, x1, y1 in resDet:
x = round((x0 + x1) / 2 / img.shape[1], 6)
y = round((y0 + y1) / 2 / img.shape[0], 6)
w = round((x1 - x0) / img.shape[1], 6)
h = round((y1 - y0) / img.shape[0], 6)
res.append(" ".join(["1", str(x), str(y), str(w), str(h)]))
open(txtfile, "w").write("\n".join(res))
cnt += 1
except:
traceback.print_exc()
